Assignment 9
Dipesh Poudel
1/8/2022
Assignment 9: Association Rule Mining
Instructions Use the “AdultUCI” data available in the “arules” package and do as follows in R Script.
1. Attach the AdultUCI data in R
library(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writedata(AdultUCI)- Check the class, structure, dimension, head and tail of the attached data and write interpretations
Checking the Class of the dataset
class(AdultUCI)## [1] "data.frame"Checking the Structure of the dataset
str(AdultUCI)## 'data.frame': 48842 obs. of 15 variables:
## $ age : int 39 50 38 53 28 37 49 52 31 42 ...
## $ workclass : Factor w/ 8 levels "Federal-gov",..: 7 6 4 4 4 4 4 6 4 4 ...
## $ fnlwgt : int 77516 83311 215646 234721 338409 284582 160187 209642 45781 159449 ...
## $ education : Ord.factor w/ 16 levels "Preschool"<"1st-4th"<..: 14 14 9 7 14 15 5 9 15 14 ...
## $ education-num : int 13 13 9 7 13 14 5 9 14 13 ...
## $ marital-status: Factor w/ 7 levels "Divorced","Married-AF-spouse",..: 5 3 1 3 3 3 4 3 5 3 ...
## $ occupation : Factor w/ 14 levels "Adm-clerical",..: 1 4 6 6 10 4 8 4 10 4 ...
## $ relationship : Factor w/ 6 levels "Husband","Not-in-family",..: 2 1 2 1 6 6 2 1 2 1 ...
## $ race : Factor w/ 5 levels "Amer-Indian-Eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 1 1 1 2 1 2 ...
## $ capital-gain : int 2174 0 0 0 0 0 0 0 14084 5178 ...
## $ capital-loss : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hours-per-week: int 40 13 40 40 40 40 16 45 50 40 ...
## $ native-country: Factor w/ 41 levels "Cambodia","Canada",..: 39 39 39 39 5 39 23 39 39 39 ...
## $ income : Ord.factor w/ 2 levels "small"<"large": 1 1 1 1 1 1 1 2 2 2 ...Checking the Dimesions of the dataset
dim(AdultUCI)## [1] 48842 15head(AdultUCI)## age workclass fnlwgt education education-num marital-status
## 1 39 State-gov 77516 Bachelors 13 Never-married
## 2 50 Self-emp-not-inc 83311 Bachelors 13 Married-civ-spouse
## 3 38 Private 215646 HS-grad 9 Divorced
## 4 53 Private 234721 11th 7 Married-civ-spouse
## 5 28 Private 338409 Bachelors 13 Married-civ-spouse
## 6 37 Private 284582 Masters 14 Married-civ-spouse
## occupation relationship race sex capital-gain capital-loss
## 1 Adm-clerical Not-in-family White Male 2174 0
## 2 Exec-managerial Husband White Male 0 0
## 3 Handlers-cleaners Not-in-family White Male 0 0
## 4 Handlers-cleaners Husband Black Male 0 0
## 5 Prof-specialty Wife Black Female 0 0
## 6 Exec-managerial Wife White Female 0 0
## hours-per-week native-country income
## 1 40 United-States small
## 2 13 United-States small
## 3 40 United-States small
## 4 40 United-States small
## 5 40 Cuba small
## 6 40 United-States smalltail(AdultUCI)## age workclass fnlwgt education education-num marital-status
## 48837 33 Private 245211 Bachelors 13 Never-married
## 48838 39 Private 215419 Bachelors 13 Divorced
## 48839 64 <NA> 321403 HS-grad 9 Widowed
## 48840 38 Private 374983 Bachelors 13 Married-civ-spouse
## 48841 44 Private 83891 Bachelors 13 Divorced
## 48842 35 Self-emp-inc 182148 Bachelors 13 Married-civ-spouse
## occupation relationship race sex capital-gain
## 48837 Prof-specialty Own-child White Male 0
## 48838 Prof-specialty Not-in-family White Female 0
## 48839 <NA> Other-relative Black Male 0
## 48840 Prof-specialty Husband White Male 0
## 48841 Adm-clerical Own-child Asian-Pac-Islander Male 5455
## 48842 Exec-managerial Husband White Male 0
## capital-loss hours-per-week native-country income
## 48837 0 40 United-States <NA>
## 48838 0 36 United-States <NA>
## 48839 0 40 United-States <NA>
## 48840 0 50 United-States <NA>
## 48841 0 40 United-States <NA>
## 48842 0 60 United-States <NA>The AdultUCI is loaded as a dataframe. There are 48842 rows(observations) and 15 columns (variables) in the dataset. Variables age fnlwgt,education-num,capital-gain,capital-loss,hours-per-week have integers values. Variables workclass, education, marital-status, occupation, race,sex, native-country, income and relationship are factors out of them income and education are ordered factors.
3. Remove “fnlwgt” and “education-num” variables from the attached data and explain the logic you have used here
# Changing dash(-) sign in column names to underscore(_)
names(AdultUCI)<-gsub("-","_",names(AdultUCI))
# Removing the said variables
AdultUCI<-subset(AdultUCI,select=c(-education_num,-fnlwgt))First of all the minus(-) sign in column name was replaced with underscore(_) sign since r interprets the minus sign as a operator. Then the columns fnlwgt and education-num variable (changed to education_num) was removed using subset command.
4. Convert “age” as ordered factor variables with cuts at 15, 25, 45, 65 and 100 and label it as “Young”, “Middle-aged”, “Senior” and “Old”
age_labels<-c("Young","Middle-aged","Senior","old")
AdultUCI$age<-cut(AdultUCI$age,breaks = c(15,25,45,65,100),labels = age_labels)
AdultUCI$age<-factor(AdultUCI$age,ordered = T,labels = age_labels)
str(AdultUCI$age)## Ord.factor w/ 4 levels "Young"<"Middle-aged"<..: 2 3 2 3 2 2 3 3 2 2 ...5. Convert the “hours-per-week” as ordered factor variable with cuts at 0, 25, 40, 60, 168 and label it as “Part-time”, “Full-time”, “Over-time” and “Workaholic”
hours_labels<-c("Part-time","Full-time","Over-time","Workaholic")
AdultUCI$hours_per_week<-cut(AdultUCI$hours_per_week,breaks = c(0,25,40,60,168),labels = hours_labels)
AdultUCI$hours_per_week<-factor(AdultUCI$hours_per_week,ordered = T,labels = hours_labels)
str(AdultUCI$hours_per_week)## Ord.factor w/ 4 levels "Part-time"<"Full-time"<..: 2 1 2 2 2 2 1 3 3 2 ...6. Convert the “capital-gain” as ordered factor variable with cuts at –Inf, 0, median and Inf and label it as “None”, “Low” and “High”
capital_gain_labels<-c("None","Low","High")
AdultUCI$capital_gain<-cut(AdultUCI$capital_gain,breaks = c(-Inf,0,median(AdultUCI[AdultUCI$capital_gain>0,]$capital_gain),Inf),labels = capital_gain_labels)
AdultUCI$capital_gain<-factor(AdultUCI$capital_gain,ordered = T,labels = capital_gain_labels)
str(AdultUCI$capital_gain)## Ord.factor w/ 3 levels "None"<"Low"<"High": 2 1 1 1 1 1 1 1 3 2 ...7. Convert the “capital-loss” as ordered factor variable with cuts at –Inf, 0, median and Inf and label it as “None”, “Low” and “High”
capital_loss_labels<-c("None","Low","High")
AdultUCI$capital_loss<-cut(AdultUCI$capital_loss,breaks = c(-Inf,0,median(AdultUCI[AdultUCI$capital_loss>0,]$capital_loss),Inf),labels = capital_loss_labels)
AdultUCI$capital_loss<-factor(AdultUCI$capital_loss,ordered = T,labels = capital_loss_labels)
str(AdultUCI$capital_loss)## Ord.factor w/ 3 levels "None"<"Low"<"High": 1 1 1 1 1 1 1 1 1 1 ...8. Create transactions of AdultUCI data as “Adult” and check it with “Adult” command
Adult<-transactions(AdultUCI)
Adult## transactions in sparse format with
## 48842 transactions (rows) and
## 115 items (columns)9. et summary of the “Adult” and interpret it critically
summary(Adult)## transactions as itemMatrix in sparse format with
## 48842 rows (elements/itemsets/transactions) and
## 115 columns (items) and a density of 0.1089939
##
## most frequent items:
## capital_loss=None capital_gain=None
## 46560 44807
## native_country=United-States race=White
## 43832 41762
## workclass=Private (Other)
## 33906 401333
##
## element (itemset/transaction) length distribution:
## sizes
## 9 10 11 12 13
## 19 971 2067 15623 30162
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.00 12.00 13.00 12.53 13.00 13.00
##
## includes extended item information - examples:
## labels variables levels
## 1 age=Young age Young
## 2 age=Middle-aged age Middle-aged
## 3 age=Senior age Senior
##
## includes extended transaction information - examples:
## transactionID
## 1 1
## 2 2
## 3 3From Summary: We have 48842 rows (elements/itemsets/transactions) and 115 columns (items). The most frequent most items have capital_loss=None, capital_gain=None.
10. Inspect head and tail of the “Adult” and interpret them carefully
inspect(head(Adult))## items transactionID
## [1] {age=Middle-aged,
## workclass=State-gov,
## education=Bachelors,
## marital_status=Never-married,
## occupation=Adm-clerical,
## relationship=Not-in-family,
## race=White,
## sex=Male,
## capital_gain=Low,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States,
## income=small} 1
## [2] {age=Senior,
## workclass=Self-emp-not-inc,
## education=Bachelors,
## marital_status=Married-civ-spouse,
## occupation=Exec-managerial,
## relationship=Husband,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Part-time,
## native_country=United-States,
## income=small} 2
## [3] {age=Middle-aged,
## workclass=Private,
## education=HS-grad,
## marital_status=Divorced,
## occupation=Handlers-cleaners,
## relationship=Not-in-family,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States,
## income=small} 3
## [4] {age=Senior,
## workclass=Private,
## education=11th,
## marital_status=Married-civ-spouse,
## occupation=Handlers-cleaners,
## relationship=Husband,
## race=Black,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States,
## income=small} 4
## [5] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Married-civ-spouse,
## occupation=Prof-specialty,
## relationship=Wife,
## race=Black,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=Cuba,
## income=small} 5
## [6] {age=Middle-aged,
## workclass=Private,
## education=Masters,
## marital_status=Married-civ-spouse,
## occupation=Exec-managerial,
## relationship=Wife,
## race=White,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States,
## income=small} 6inspect(tail(Adult))## items transactionID
## [1] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Never-married,
## occupation=Prof-specialty,
## relationship=Own-child,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States} 48837
## [2] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Divorced,
## occupation=Prof-specialty,
## relationship=Not-in-family,
## race=White,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States} 48838
## [3] {age=Senior,
## education=HS-grad,
## marital_status=Widowed,
## relationship=Other-relative,
## race=Black,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States} 48839
## [4] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Married-civ-spouse,
## occupation=Prof-specialty,
## relationship=Husband,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Over-time,
## native_country=United-States} 48840
## [5] {age=Middle-aged,
## workclass=Private,
## education=Bachelors,
## marital_status=Divorced,
## occupation=Adm-clerical,
## relationship=Own-child,
## race=Asian-Pac-Islander,
## sex=Male,
## capital_gain=Low,
## capital_loss=None,
## hours_per_week=Full-time,
## native_country=United-States} 48841
## [6] {age=Middle-aged,
## workclass=Self-emp-inc,
## education=Bachelors,
## marital_status=Married-civ-spouse,
## occupation=Exec-managerial,
## relationship=Husband,
## race=White,
## sex=Male,
## capital_gain=None,
## capital_loss=None,
## hours_per_week=Over-time,
## native_country=United-States} 48842Each row is given a transactionID and values of each row is converted into a list of items in a transaction.
11 Create absolute and relative item frequency plot and color it with RColorBrewer package
library(RColorBrewer)
palette = brewer.pal(10,'RdYlBu');
# Absolute Frequency Plot
itemFrequencyPlot(Adult,
type="absolute",
topN=10,
col=palette,
main="Absolute Frequency Plot",
xlab="Items"
)
itemFrequencyPlot(Adult,
type="relative",
topN=10,
col=palette,
main="Relative Frequency Plot",
xlab="Items")
12. Create an apriori rule as “association.rule” with support = 1%, confidence = 80% and maximum length of the rule as 10. Get summary of this rule and interpret it carefully.
association.rule<-apriori(Adult,
parameter = list(supp=0.01,conf=0.8,maxlen=10,target= "rules")
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 1
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 488
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[115 item(s), 48842 transaction(s)] done [0.07s].
## sorting and recoding items ... [67 item(s)] done [0.01s].
## creating transaction tree ... done [0.04s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen
## = 10, : Mining stopped (maxlen reached). Only patterns up to a length of 10
## returned!## done [1.15s].
## writing ... [197371 rule(s)] done [0.04s].
## creating S4 object ... done [0.07s].summary(association.rule)## set of 197371 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4 5 6 7 8 9 10
## 4 266 3303 15219 37015 53616 48402 27754 9827 1965
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 5.000 6.000 6.318 7.000 10.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.01001 Min. :0.8000 Min. :0.01001 Min. : 0.8677
## 1st Qu.:0.01251 1st Qu.:0.8953 1st Qu.:0.01353 1st Qu.: 1.0059
## Median :0.01708 Median :0.9372 Median :0.01847 Median : 1.0398
## Mean :0.02726 Mean :0.9283 Mean :0.02949 Mean : 1.2899
## 3rd Qu.:0.02766 3rd Qu.:0.9669 3rd Qu.:0.02995 3rd Qu.: 1.2160
## Max. :0.95328 Max. :1.0000 Max. :1.00000 Max. :20.6826
## count
## Min. : 489
## 1st Qu.: 611
## Median : 834
## Mean : 1331
## 3rd Qu.: 1351
## Max. :46560
##
## mining info:
## data ntransactions support confidence
## Adult 48842 0.01 0.8
## call
## apriori(data = Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen = 10, target = "rules"))We got 197371 rules. We can also see number of rules with different numbers of items. The rules with support count less than 488 was discarded.
13. Inspect the first 10 rules and interpret it critically.
inspect(head(association.rule,10))## lhs rhs support confidence coverage lift count
## [1] {} => {race=White} 0.85504279 0.8550428 1.00000000 1.0000000 41762
## [2] {} => {native_country=United-States} 0.89742435 0.8974243 1.00000000 1.0000000 43832
## [3] {} => {capital_gain=None} 0.91738668 0.9173867 1.00000000 1.0000000 44807
## [4] {} => {capital_loss=None} 0.95327792 0.9532779 1.00000000 1.0000000 46560
## [5] {education=5th-6th} => {capital_loss=None} 0.01009377 0.9685658 0.01042136 1.0160372 493
## [6] {education=Doctorate} => {race=White} 0.01076942 0.8855219 0.01216166 1.0356463 526
## [7] {education=Doctorate} => {capital_loss=None} 0.01076942 0.8855219 0.01216166 0.9289231 526
## [8] {marital_status=Married-spouse-absent} => {capital_gain=None} 0.01218214 0.9474522 0.01285779 1.0327730 595
## [9] {marital_status=Married-spouse-absent} => {capital_loss=None} 0.01240735 0.9649682 0.01285779 1.0122632 606
## [10] {education=12th} => {native_country=United-States} 0.01140412 0.8477930 0.01345154 0.9446958 557We can see that there are empty rules as well which is not of interest to us so we can remove them.
14. Remove the empty rules from the “association.rule” and inspect the first 10 rules with interpretations.
association.rule<-apriori(Adult,
parameter = list(supp=0.01,conf=0.8,maxlen=10,minlen=2,target= "rules")
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 488
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[115 item(s), 48842 transaction(s)] done [0.05s].
## sorting and recoding items ... [67 item(s)] done [0.01s].
## creating transaction tree ... done [0.04s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen
## = 10, : Mining stopped (maxlen reached). Only patterns up to a length of 10
## returned!## done [1.03s].
## writing ... [197367 rule(s)] done [0.04s].
## creating S4 object ... done [0.06s].inspect(head(association.rule[1:10]))## lhs rhs support confidence coverage lift count
## [1] {education=5th-6th} => {capital_loss=None} 0.01009377 0.9685658 0.01042136 1.0160372 493
## [2] {education=Doctorate} => {race=White} 0.01076942 0.8855219 0.01216166 1.0356463 526
## [3] {education=Doctorate} => {capital_loss=None} 0.01076942 0.8855219 0.01216166 0.9289231 526
## [4] {marital_status=Married-spouse-absent} => {capital_gain=None} 0.01218214 0.9474522 0.01285779 1.0327730 595
## [5] {marital_status=Married-spouse-absent} => {capital_loss=None} 0.01240735 0.9649682 0.01285779 1.0122632 606
## [6] {education=12th} => {native_country=United-States} 0.01140412 0.8477930 0.01345154 0.9446958 557We see that there are no more empty rules. We are only seeing rules with one item on LHS and one on RHS.
15. Create a new rule as “capital.gain.rhs.rule” with “capital-gain=None” in the RHS with support of 1%, confidence of 80%, maximum length of 10 and minimum length of 2.
capital.gain.rhs.rule<-apriori(Adult,
parameter=list(supp=0.01,conf=0.8,maxlen=10,minlen=2),
appearance=list(default="lhs",rhs="capital_gain=None")
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 488
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[115 item(s), 48842 transaction(s)] done [0.05s].
## sorting and recoding items ... [67 item(s)] done [0.01s].
## creating transaction tree ... done [0.05s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen
## = 10, : Mining stopped (maxlen reached). Only patterns up to a length of 10
## returned!## done [0.98s].
## writing ... [35433 rule(s)] done [0.01s].
## creating S4 object ... done [0.03s].16. Get summary of this rule and interpret it critically.
summary(capital.gain.rhs.rule)## set of 35433 rules
##
## rule length distribution (lhs + rhs):sizes
## 2 3 4 5 6 7 8 9 10
## 60 706 3062 7110 9790 8377 4537 1508 283
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 5.000 6.000 6.212 7.000 10.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.01001 Min. :0.8000 Min. :0.01009 Min. :0.8720
## 1st Qu.:0.01265 1st Qu.:0.9015 1st Qu.:0.01359 1st Qu.:0.9827
## Median :0.01744 Median :0.9453 Median :0.01882 Median :1.0304
## Mean :0.02819 Mean :0.9287 Mean :0.03051 Mean :1.0124
## 3rd Qu.:0.02864 3rd Qu.:0.9654 3rd Qu.:0.03092 3rd Qu.:1.0523
## Max. :0.87066 Max. :1.0000 Max. :0.95328 Max. :1.0901
## count
## Min. : 489
## 1st Qu.: 618
## Median : 852
## Mean : 1377
## 3rd Qu.: 1399
## Max. :42525
##
## mining info:
## data ntransactions support confidence
## Adult 48842 0.01 0.8
## call
## apriori(data = Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen = 10, minlen = 2), appearance = list(default = "lhs", rhs = "capital_gain=None"))We filtered out the rules where value of capital_gain is None in RHS. We have 35433 such rules.
17. Create a new rule as “hours.per.week.ft.rule” with “hour-per-week=Full-time” in the RHS with support of 1%, confidence of 80%, maximum length of 10 and minimum length of 2.
hours.per.week.ft.rule<-apriori(Adult,
parameter=list(supp=0.01,conf=0.8,maxlen=10,minlen=2),
appearance=list(default="lhs",rhs="hours_per_week=Full-time")
)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.01 2
## maxlen target ext
## 10 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 488
##
## set item appearances ...[1 item(s)] done [0.00s].
## set transactions ...[115 item(s), 48842 transaction(s)] done [0.07s].
## sorting and recoding items ... [67 item(s)] done [0.01s].
## creating transaction tree ... done [0.07s].
## checking subsets of size 1 2 3 4 5 6 7 8 9 10## Warning in apriori(Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen
## = 10, : Mining stopped (maxlen reached). Only patterns up to a length of 10
## returned!## done [1.42s].
## writing ... [159 rule(s)] done [0.01s].
## creating S4 object ... done [0.02s].18. Get summary of this rule and interpret it critically.
summary(hours.per.week.ft.rule)## set of 159 rules
##
## rule length distribution (lhs + rhs):sizes
## 3 4 5 6 7 8
## 3 16 48 58 29 5
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.000 5.000 6.000 5.686 6.000 8.000
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.01001 Min. :0.8000 Min. :0.01216 Min. :1.367
## 1st Qu.:0.01066 1st Qu.:0.8047 1st Qu.:0.01318 1st Qu.:1.375
## Median :0.01179 Median :0.8086 Median :0.01456 Median :1.382
## Mean :0.01237 Mean :0.8089 Mean :0.01530 Mean :1.383
## 3rd Qu.:0.01331 3rd Qu.:0.8129 3rd Qu.:0.01654 3rd Qu.:1.389
## Max. :0.01992 Max. :0.8266 Max. :0.02471 Max. :1.413
## count
## Min. :489.0
## 1st Qu.:520.5
## Median :576.0
## Mean :604.4
## 3rd Qu.:650.0
## Max. :973.0
##
## mining info:
## data ntransactions support confidence
## Adult 48842 0.01 0.8
## call
## apriori(data = Adult, parameter = list(supp = 0.01, conf = 0.8, maxlen = 10, minlen = 2), appearance = list(default = "lhs", rhs = "hours_per_week=Full-time"))We have filtered the rules which contains hours_per_week=Full-time. There are 159 such rules. The minimum number of items (lhs+rhs) in the selected rules is 3 we have 3 such rules. Similarly we have 16,48,58,29,5 rules with 4,5,6,7 and 8 items respectively.
19. Get new rule of “hours.per.week.ft.rule” as “conf.sort.rule” by sorting “hours.per.week.ft.rule” in descending order by “confidence” and inspect the head and tail rules with critical interpretation.
conf.sort.rule<-sort(hours.per.week.ft.rule,by="confidence",decreasing = TRUE)inspect(head(conf.sort.rule))## lhs rhs support confidence coverage lift count
## [1] {age=Middle-aged,
## occupation=Adm-clerical,
## relationship=Unmarried,
## sex=Female,
## capital_gain=None} => {hours_per_week=Full-time} 0.01005282 0.8265993 0.01216166 1.412771 491
## [2] {age=Middle-aged,
## occupation=Adm-clerical,
## relationship=Unmarried,
## capital_gain=None} => {hours_per_week=Full-time} 0.01066705 0.8243671 0.01293968 1.408956 521
## [3] {age=Middle-aged,
## occupation=Adm-clerical,
## relationship=Unmarried,
## capital_gain=None,
## capital_loss=None} => {hours_per_week=Full-time} 0.01042136 0.8236246 0.01265304 1.407687 509
## [4] {age=Middle-aged,
## relationship=Unmarried,
## race=Black,
## sex=Female,
## capital_gain=None} => {hours_per_week=Full-time} 0.01029851 0.8218954 0.01253020 1.404732 503
## [5] {age=Middle-aged,
## workclass=Private,
## education=HS-grad,
## race=Black,
## capital_gain=None,
## capital_loss=None} => {hours_per_week=Full-time} 0.01148602 0.8201754 0.01400434 1.401792 561
## [6] {age=Middle-aged,
## education=HS-grad,
## occupation=Adm-clerical,
## sex=Female,
## capital_gain=None,
## capital_loss=None} => {hours_per_week=Full-time} 0.01031899 0.8195122 0.01259162 1.400658 504We have got 6 rules with highest confidence value above the threshold. Each rule has high confidence and lift>1 which means these rules are of high interest to us.
inspect(tail(conf.sort.rule))## lhs rhs support confidence coverage lift count
## [1] {occupation=Adm-clerical,
## relationship=Unmarried} => {hours_per_week=Full-time} 0.01756685 0.8003731 0.02194832 1.367947 858
## [2] {workclass=Private,
## occupation=Adm-clerical,
## relationship=Unmarried,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## native_country=United-States} => {hours_per_week=Full-time} 0.01033946 0.8003170 0.01291921 1.367851 505
## [3] {occupation=Adm-clerical,
## relationship=Unmarried,
## sex=Female,
## capital_gain=None,
## capital_loss=None,
## income=small} => {hours_per_week=Full-time} 0.01042136 0.8003145 0.01302158 1.367847 509
## [4] {occupation=Machine-op-inspct,
## sex=Female,
## capital_gain=None,
## native_country=United-States} => {hours_per_week=Full-time} 0.01031899 0.8000000 0.01289873 1.367309 504
## [5] {occupation=Machine-op-inspct,
## sex=Female,
## capital_loss=None,
## native_country=United-States} => {hours_per_week=Full-time} 0.01040088 0.8000000 0.01300111 1.367309 508
## [6] {workclass=Private,
## education=HS-grad,
## race=Black,
## capital_gain=None,
## native_country=United-States,
## income=small} => {hours_per_week=Full-time} 0.01171123 0.8000000 0.01463904 1.367309 572We have got 6 rules with lowest confidence value above the threshold. Even though the value of confidence is lower the value of lift>1 which means these rules are also of high interest to us.
- Plot the “hours.per.week.ft.rule” with arulesViz package with plot, plot with “two-key plot”, engine=”plotly”, method=graph & engine=htmlwidget and parallel coordinate plot and interpret each graph carefully.
library(arulesViz)
library(plotly)## Loading required package: ggplot2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplot(hours.per.week.ft.rule,main="Visulizing Rules obtained in hours.per.week.ft.rule")## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
This is a scatter plot of support and confidence values. In the graph, lighter the color of the point(dot) in the graph lower the value of lift.
plot(hours.per.week.ft.rule,method = "two-key plot",main="Two Key Plot")## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
This graph shows the scatter plot of support and confidence. The different color shows the number of items(lhs+rhs) in the rule.
plot(hours.per.week.ft.rule,engine = "plotly",main="Intercative Plot")## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.Using engine=“plotly” we have created a interactive scatter plot of support and confidence. The lighter dot means lower value of lift.
plot(hours.per.week.ft.rule,engine = "htmlwidget",method = "graph")## Warning: Too many rules supplied. Only plotting the best 100 using
## 'lift' (change control parameter max if needed).Using htmlwidget engine we get the graph of items and rules in graph format.
plot(hours.per.week.ft.rule,method = "paracoord")
Parallel coordinate system is used for visulizing the multi-dimesion in 2d. This graph does not make much sense in association rule mining.